Interactive Exploration of Fuzzy Clusters

In this chapter we describe methods that assist the user to visually explore fuzzy clusters. We focus on a supervised approach to generate clusters for classes of interest of a given data set. The algorithm constructs local, one-dimensional neighborhood models, so-called Neighborgrams, for objects of the classes of interest that serve as a set of potential cluster candidates. The presented algorithm automatically chooses the best subset of Neighborgrams, but, more importantly, the accompanying visualization allows the user to fine-tune the clustering process by visually selecting, discarding, or adjusting potential cluster candidates. We also show how the algorithm can be applied to problems where multiple descriptions of data are available. This type of data can be found in biological data analysis for example, where often several different descriptors for the same molecule exist but each individual descriptor is only able to model parts of the data.